EP3102700A1 - Test de diagnostic moléculaire pour prédire une réponse à des médicaments anti-angiogéniques et pronostic du cancer - Google Patents

Test de diagnostic moléculaire pour prédire une réponse à des médicaments anti-angiogéniques et pronostic du cancer

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Publication number
EP3102700A1
EP3102700A1 EP15705693.8A EP15705693A EP3102700A1 EP 3102700 A1 EP3102700 A1 EP 3102700A1 EP 15705693 A EP15705693 A EP 15705693A EP 3102700 A1 EP3102700 A1 EP 3102700A1
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European Patent Office
Prior art keywords
sample
expression
biomarkers
subject
biomarker
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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EP15705693.8A
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German (de)
English (en)
Inventor
Denis Paul Harkin
Richard Kennedy
Katherine E. Keating
Andrena MCCAVIGAN
Laura A HILL
Steve Deharo
Timothy Davison
Fionnuala Patterson
Sinead DONEGAN
Gera JELLEMA
Charlie GOURLEY
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Almac Diagnostic Services Ltd
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Almac Diagnostics Ltd
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Publication of EP3102700A1 publication Critical patent/EP3102700A1/fr
Withdrawn legal-status Critical Current

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to a molecular diagnostic test useful for providing a prognosis and directing treatment of cancers from different anatomical sites.
  • the invention includes the derivation of a gene classification model from gene expression levels.
  • One application is the selection of whether to administer certain therapeutics, such as anti- angiogeneic therapeutics, to subjects receiving a standard of care cancer therapy.
  • Another application is the stratification of cancer patients into those that have a good clinical prognosis or poor clinical prognosis.
  • the present invention provides a test that can guide therapy selection as well as selecting patient groups for enrichment strategies during clinical trial evaluation of novel therapeutics.
  • the invention can be used as a prognostic indicator for certain cancers including ovarian cancer, breast cancer, colon, prostate, lung and glioblastoma.
  • the angiogenesis subtype can be identified from fresh/frozen (FF) or formalin fixed paraffin embedded FFPE patient samples.
  • Ovarian cancer is the leading cause of death among all gynecological cancers in western countries. This high death rate is due to the diagnosis at an advanced stage in most patients.
  • Epithelial ovarian cancer constitutes 90% of ovarian malignancies and is classified into distinct histologic categories including serous, mucinous, endometrioid, clear cell, transitional, mixed, and undifferentiated subtypes. There is increasing evidence that these differed histologies arise from different aetiologies.
  • microarrays and molecular genomics have the potential for a significant impact on the diagnostic capability and prognostic classification of disease, which may aid in the prediction of the response of an individual patient to a defined therapeutic regimen.
  • Microarrays provide for the analysis of large amounts of genetic information, thereby providing a genetic fingerprint of an individual. There is much enthusiasm that this technology will ultimately provide the necessary tools for custom-made drug treatment regimens.
  • Angiogenesis is a key component of neo-vascularisation of tumors and essential to tumorigenesis and metastatsis. As such, it is a key area for therapeutic intervention and has been correlated to poor prognosis and reduced survival. This has promoted the development of a number of agents that target angiogenesis related processes and pathways, including the market leader and first FDA-approved anti-angiogenic, bevacizumab (Avastin), produced by Genentech/Roche .
  • biomarker of response to bevacizumab would improve assessment of treatment outcomes and thus enable the identification of patient subgroups that would receive the most clinical benefit from bevacizumab treatment. This would be particularly relevant in the case of metastatic breast cancer, where the absence of a clinically beneficial biomarker has undermined the use of bevacizumab. Thus far, no such biomarker has been clinically validated to predict bevacizumab efficacy. Hypertension and VEGF polymorphisms are so far the only biomarkers to show potential, but important questions remain about their use in a clinical setting.
  • Another approach to anti-angiogenic therapy is simulataneous targeting of multiple angiogenic pathways rather than selective targeting of the VEGF pathway.
  • multitargeted anti-angiogenic agents should more completely inhibit angiogenesis than agents such as bevacizumab and thus may produce greater therapeutic benefit. It has been postulated that in some tumors, angiogenesis may require VEGF only in the early stages of disease but is driven by additional angiogenic pathways as the disease progresses. Therefore, by targeting multiple pathways, it may be possible to counteract compensatory escape mechanisms that could lead to resistance to VEGF inhibition.
  • biomarkers or a collection of biomarkers expressed in cancer that identify a subtype of cancer that is associated with an up- regulation in molecular signaling related to immune response and a down-regulation in molecular signaling related to angiogenesis and vasculature development, referred to herein as a "non-angiogenesis" or “immune” subtype.
  • the collection of biomarkers may be defined by an expression signature, and the expression signature is used to assign a cumulative score to the measured expression values of the collection of biomarkers.
  • biomarkers and expression signatures may form the basis of a single parameter or multiparametric predictive test that could be delivered using methods known in the art such as microarray, next generation sequencing (NGS), Q-PCR, immunohistochemistry, ELISA or other technologies that can quantify mRNA or protein expression.
  • NGS next generation sequencing
  • Q-PCR Q-PCR
  • immunohistochemistry immunohistochemistry
  • ELISA immunohistochemistry
  • the cancer subtypes described herein are common to many types of cancer and are not limited to a single cancer disease type. Accordingly, the expression signatures of the present invention are not limited to a single cancer type.
  • the non-angiogenesis expression signature comprises two or more biomarkers selected from the biomarkers listed in Tables 1. In another example embodiment, the non- angiogenesis expression signature comprises two or more biomarkers listed in Table 2 or 3. In certain other example embodiments, the expression signature comprises one or more of IGF2, SOX11, INS, CXCL17, SLC5A1, TMEM45A, CXCR2PA, MFAP2, MATN3, or RTP4.
  • the expression signature comprises one or more of IGF2, CDR1, COL3A1, SPARC, TIMP3, INS, COL8A1, NUAK1, MATN3, and TMEM45A. In another example embodiment, the expression signature comprises one or more of INS, SPARC, COL8A1, COL3A1, CDR1, NUAK1, TIMP3, and MMP14.
  • the non-angiogenesis signature comprises the biomarkers listed in Table 2 and their corresponding weights as determined using a PLS classifier. In another example embodiment, the non-angiogenesis signature comprises the biomarkers listed in Table 3 and their corresponding ranks within a decision function.
  • the invention provides a method for selecting whether to administer an anti-angiogenic therapeutic agent to a subject, comprising: measuring the expression level(s) of one or more biomarkers selected from Table 2 or Table 3 in a sample from the subject; assessing from the expression level(s) of the one or more biomarkers whether the sample from the subject is positive or negative for a biomarker signature, wherein if the sample is positive for the biomarker signature an anti-angiogenic therapeutic agent is contraindicated.
  • assessing whether the sample is positive or negative for the biomarker signature comprises: determining a sample expression score for the one or more biomarkers; comparing the sample expression score to a threshold score; and determining whether the sample expression score is above or equal to the threshold expression score, wherein if the sample expression score is above or equal to the threshold score the sample is positive for the biomarker signature.
  • the subject is receiving or has received treatment with a chemotherapeutic agent.
  • the invention provides a method of treating cancer comprising administering a chemotherapeutic agent and not administering an anti-angiogenic therapeutic agent to a subject wherein the subject is selected for treatment on the basis of a method as described herein and the subject is positive for the biomarker signature.
  • a method of treating cancer comprising administering a chemotherapeutic agent and not administering an anti-angiogenic therapeutic agent to a subject wherein the subject is selected for treatment by measuring the expression level(s) of one or more biomarkers selected from Table 2 or Table 3 in a sample from the subject; assessing from the expression levels of the biomarkers whether the sample from the subject is positive or negative for a biomarker signature, wherein if the sample is postive for the biomarker signature the subject is selected for treatment.
  • the present invention relates to a chemotherapeutic agent for use in treating cancer in a subject wherein the subject is selected for treatment on the basis of a method as described herein and is positive for the biomarker signature and wherein the subject is not treated with an anti-angiogenic therapeutic agent.
  • the invention also relates to a chemotherapeutic agent for use in treating cancer in a subject wherein the subject is selected for treatment by measuring the expression level(s) of one or more biomarkers selected from Table 2 or Table 3 in a sample from the subject; assessing from the expression levels of the biomarkers whether the sample from the subject is positive or negative for a biomarker signature, wherein if the sample is positive for the biomarker signature the subject is selected for treatment and wherein the subject is not treated with an anti-angiogenic therapeutic agent.
  • the present invention relates to a method of treating cancer comprising administering a chemotherapeutic agent to a subject wherein the subject is positive for a biomarker signature defined by the expression levels of one or more biomarkers selected from Table 2 or Table 3 and wherein an anti-angiogenic therapeutic agent is not administered.
  • the present invention relates to a chemotherapeutic agent for use in treating cancer in a subject wherein the subject is positive for a biomarker signature defined by the expression levels of one or more biomarkers selected from Table 2 or Table 3 and wherein the subject is not treated with an anti-angiogenic therapeutic agent.
  • the chemotherapeutic agent comprises a platinum-based chemotherapeutic agent, an alkylating agent, an anti-metabolite (such as 5FU), an anti-tumor antibiotic, a topoisomerase inhibitor, a mitotic inhibitor, or a combination thereof.
  • the chemotherapeutic agent may comprise a platinum based-chemotherapeutic agent, a mitotic inhibitor, or a combination thereof.
  • the chemotherapeutic agent comprises carboplatin and/or paclitaxel.
  • the chemotherapeutic agent may reflect the standard of care treatment for the cancer. The standard of care treatment may differ for different types of cancer - for example carboplatin in ovarian cancer, 5FU in colorectal cancer, platinum in head and neck cancer.
  • assessing whether the sample is positive or negative for the biomarker signature may comprise determining a sample expression score for the one or more biomarkers; comparing the sample expression score to a threshold score; and determining whether the sample expression score is above or equal to the threshold expression score, wherein if the sample expression score is above or equal to the threshold score the sample is positive for the biomarker signature.
  • the subject may be suffering from cancer.
  • the cancer may be ovarian cancer, optionally high grade serous ovarian cancer.
  • administering an agent is used interchanging with “treating with” an agent.
  • a method for determing clinical prognosis of a subject with cancer comprising: measuring the expression level(s) of one or more biomarkers selected from Table 2 or Table 3 in a sample from the subject; assessing from the expression level(s) of the one or more biomarkers whether the sample from the subject is positive or negative for a biomarker signature, wherein if the sample is positive for the biomarker signature the subject has a good prognosis.
  • Assessing whether the sample is positive or negative for the biomarker signature may comprise: determining a sample expression score for the biomarkers; comparing the sample expression score to a threshold score; and determining whether the sample expression score is above or equal to the threshold expression score, wherein if the sample expression score is above or equal to the threshold score the sample is positive for the biomarker signature.
  • the good prognosis indicates increased progression free survival or overall survival rates compared to samples that are negative for the biomarker signature, optionally compared to samples with a sample expression score below the threshold score.
  • the subject is receiving, has received and/or will receive chemotherapeutic treatment and/or will not receive treatment with an anti- angiogenic therapeutic agent.
  • the chemotherapeutic treatment may comprise administration of a platinum-based chemotherapeutic agent, an alkylating agent, an antimetabolite, an anti-tumor antibiotic, a topoisomerase inhibitor, a mitotic inhibitor, or a combination thereof.
  • the chemotherapeutic treatment may comprise administration of a platinum-based chemotherapeutic agent, a mitotic inhibitor, or a combination thereof.
  • the chemotherapeutic treatment comprises administration of paclitaxel and carboplatin.
  • the cancer may be ovarian cancer or colorectal cancer
  • the present invention relates to a method for selecting whether to administer Bevacizumab to a subject, comprising: in a test sample obtained from a subject suffering from ovarian cancer, which subject is being, has been and/or will be treated using a platinum-based chemotherapeutic agent and/or a mitotic inhibitor; measuring expression levels of one, two or more, up to all of the, biomarkers selected from Table 2; assessing from the expression level(s) of the one, two or more biomarkers whether the sample from the subject is positive or negative for a biomarker signature, selecting a treatment based on whether the sample is positive for the biomarker signature, wherein is the sample is positive for the biomarker signature Bevacizumab is contraindicated.
  • assessing whether the sample is positive or negative for the biomarker signature comprises determining a sample expression score for the one, two or more biomarkers; comparing the sample expression score to a threshold score; and determining whether the sample expression score is above or equal to the threshold expression score, wherein if the sample expression score is above or equal to the threshold score the sample is positive for the biomarker signature.
  • the invention also relates to a method for determining clinical prognosis of a subject, comprising: (a) in a test sample obtained from a subject suffering from ovarian cancer, which subject is being, has been and/or will be treated using a platinum-based chemotherapeutic agent and/or a mitotic inhibitor; (b) measuring expression levels of one or more, up to all of the, biomarkers selected from Table 2 or Table 3; (c) assessing from the expression level(s) of the one or more biomarkers whether the sample from the subject is positive or negative for a biomarker signature, wherein if the sample is positive for the biomarker signature the subject has a good prognosis.
  • Assessing whether the sample is positive or negative for the biomarker signature may comprise: (i) determining a sample expression score for the one or more biomarkers; (ii) comparing the sample expression score to a threshold score; and (iii) determining whether the sample expression score is above or equal to the threshold expression score, wherein if the sample expression score is above or equal to the threshold score the sample is positive for the biomarker signature.
  • the good prognosis indicates increased progression free survival or overall survival rates compared to samples that are negative for the biomarker signature, optionally compared to samples with a sample expression score below the threshold score.
  • the expression level(s) of two or more biomarkers selected from Table 2 or Table 3 may be measured in a sample from the subject.
  • a classification tree is built through a process called binary recursive partitioning, which is an iterative procedure of splitting the data into partitions/branches. The goal is to build a tree that distinguishes among pre-defined classes.
  • Each node in the tree corresponds to a variable. To choose the best split at a node, each variable is considered in turn, where every possible split is tried and considered, and the best split is the one which produces the largest decrease in diversity of the classification label within each partition.
  • a method for selecting whether to administer an anti-angiogenic therapeutic agent to a subject using the expression signatures disclosed herein comprising obtaining a test sample from the subject, measuring expression levels of a biomarker panel from the test sample, determining a sample expression score for the biomarker panel, comparing the sample expression score to a threshold score, and selecting a treatment based on whether the expression score is equal to or above the threshold score.
  • a sample expression score is equal to or above the threshold score indicates an anti-angiogenic agent is contraindicated and should not be administered to the subject.
  • a sample expression score below the threshold score indicates an anti-angiogenic agent is not contraindicated and can be administered to the subject.
  • a therapeutic agent is "contraindicated” or “detrimental” to a patient if the cancer's rate of growth is accelerated as a result of contact with the therapeutic agent, compared to its growth in the absence of contact with the therapeutic agent. Growth of a cancer can be measured in a variety of ways. For instance, the size of a tumor, or measuring the expression of tumor markers appropriate for that tumor type.
  • a therapeutic agent can also be considered “contraindicated” or “detrimental” if the patient' s overall prognosis (progression free survival and overall survival) is reduced by the administration of the therapeutic agent.
  • the expression signature disclosed herein may determine a patient' s clinical prognosis upon administration of an anti-angiogeneic agent following standard cancer therapy.
  • the subject suffers from cancer.
  • the cancer may include, but is not limited to, ovarian cancer, breast cancer, colon cancer, colorectal cancer, glioblastoma, kidney cancer, including renal cell carcinoma, heatocelluar cancer, thyroid cancer, pancreatic cancer, neuroendocrine cancer, esophageal cancer, gastrointestinal stromal tumors (GIST), gastric cancer, liver cancer, including adult primary liver cancer, lymphoma, melanoma, or multiple myeloma.
  • the cancer is ovarian cancer.
  • the ovarian cancer is high grade serous ovarian cancer.
  • the patient may have received, is receiving and/or will receive a treatment which may be a standard of care treatment for the cancer type of the subject.
  • that treatment which may be a standard of care treatment may include treatment with a chemotherapeutic agent.
  • the chemotherapeutic treatment may include administration of a platinum-based chemotherapeutic agent, an alkylating agent, an antimetabolite, an anti-tumor antibiotic, a topoisomerase inhibitor, a mitotic inhibitor, or a combination thereof.
  • the chemotherapeutic treatment comprises administration of a platinum-based chemotherapeutic agent, a mitotic inhibitor, or a combination thereof. In certain other example embodiments, the chemotherapeutic treatment comprises administration of carboplatin and paclitaxel.
  • the subject has high grade serous ovarian cancer and has previously received a platinum-based chemotherapeutic agent and a mitotic inhibitor. In another example embodiment, the subject has high grade serous ovarian cancer and has previously received carboplatin and paclitaxel.
  • the anti-angiogenic therapeutic agent may be a VEGF-pathway-targeted therapeutic agent (such as bevacizumab or aflibercept), an angiopoietin-TIE2 pathway inhibitor, an endogenous angiogenic inhibitor, or an immunomodulatory agent.
  • the anti- angiogenic therapeutic agent is a VEGF-pathway-targeted therapeutic agent.
  • the anti-angiogenic therapeutic agent is bevacizumab.
  • a method for determining a clinical prognosis of a subject using the expression signatures disclosed herein comprising obtaining a test sample from the subject, measuring expression levels of a biomarker panel from the test sample, determining a sample expression score for the biomarker panel, comparing the sample expression score to the threshold expression score, wherein if the expression score is equal to or above the threshold expression score the clinical prognosis is a good prognosis.
  • a good prognosis indicates increased survival rates compared to a subject with an expression score below the threshold score.
  • the subject suffers from cancer.
  • the cancer may include, but is not limited to, ovarian cancer, breast cancer, colon cancer, colorectal cancer, or glioblastoma.
  • the cancer is ovarian cancer.
  • the ovarian cancer is high grade serous ovarian cancer.
  • the subject may receive, has received and/or will receive a chemotherapeutic treatment.
  • the chemotherapeutic treatment may include administration of a platinum-based chemotherapeutic agent, an alkylating agent, an anti-metabolite, an anti-tumor antibiotic, a topoisomerase inhibitor, a mitotic inhibitor, or a combination thereof.
  • the chemotherapeutic treatment comprises administration of a platinum-based chemotherapeutic agent, a mitotic inhibitor, or a combination thereof. In certain other example embodiments, the chemotherapeutic treatment comprises administration of carboplatin and paclitaxel.
  • the subject has high grade serous ovarian cancer and may receive, has received and/or will receive a platinum-based chemotherapeutic agent and a mitotic inhibitor, such as taxane. In another example embodiment, the subject has high grade serous ovarian cancer and is may receive, has received, and/or will receive carboplatin and paclitaxel.
  • kits for conventional diagnostic uses listed above such as qPCR, NGS, microarray, and immunoassays such as immunohistochemistry, ELISA, Western blot and the like.
  • kits for conventional diagnostic uses listed above such as qPCR, NGS, microarray, and immunoassays such as immunohistochemistry, ELISA, Western blot and the like.
  • kits include appropriate reagents and directions to assay the expression of the genes or gene products and quantify rnPvNA or protein expression.
  • such methods may be used to identify patients that are sensitive to and respond to drugs that inhibit, either directly or indirectly, processes relating to angiogenesis.
  • such methods may be used to identify patients that are resistant to or do not respond or will respond in adverse fashion to drugs that inhibit, either directly or indirectly, processes relating to angiogenesis.
  • This invention also relates to guiding effective treatment of patients. Further, methods relating to selection of patient treatment regimens and selecting patients for clinical trials of current, or developmental stage drugs that directly or indirectly affect angiogenesis are provided.
  • FFPE formalin fixed paraffin-embedded
  • FF fresh/frozen
  • Figure 1 provides a heat map showing unsupervised hierarchical clustering of gene expression data in 265 high grade serous ovarian carcinomas. Each column represents the expression of these probe sets in one tumor. Probe set expression across all clusters is represented horizontally. The bar above the heat map is color-coded by cluster as described in the legend box. The second bar is color-coded for class label as described in the legend box. Functional processes corresponding to each probe set cluster are labeled to the right of the figure.
  • Figure 2 provides Kaplan-Meier analysis of overall survival by cluster from unsupervised analysis of gene expression in 265 high grade serous ovarian carcinomas.
  • Figure 3 provides Kaplan-Meier analysis of survival of the two classes defined by the 63 -gene signature classifier in the Edinburgh (discovery) dataset.
  • Proangiogenic group consists of Angio and Angioimmune subgroups.
  • Figure 4 provides Kaplan-Meier analysis of survival of the two classes defined by the 63-gene signature classifier in the Tothill (validation) dataset.
  • Proangiogenic group consists of Angio and Angioimmune subgroups.
  • Figure 5 provides Kaplan Meier curves for progression free survival in Immune
  • Figure 6 provides Kaplan Meier curves for overall survival in Immune ( Figure
  • Figure 7 provides Kaplan Meier curves for progression free survival (A) and overall survival (B) for carboplatin and paclitaxel treated ICON7 trail patients defined by the 63 gene signature.
  • Figure 8A and 8B are graphs demonstrating certain classification performance benchmarks of an example non-angiogenesis signature as applied to colorectal cancer samples.
  • Figure 9 Signature development: AUC of training set under CV.
  • Figure 10 Signature development: C-Index of training set under CV.
  • Figure 11 Signature development: HR of training set under CV.
  • Figure 12 Signature development: HR of ICON7 SOC samples under CV.
  • Figure 13 Signature development: C-Index of ICON7 SOC samples under CV.
  • Figure 14 Signature development: HR of ICON7 Immune samples under CV.
  • Figure 15 Signature development: HR of ICON7 ProAngio samples under CV.
  • Figure 16 Core set analysis: lmmune63GeneSig_CoreGenes_InternalVal.png.
  • Figure 17 Core set analysis: Immune63GeneSig_CoreGenes_Tothill.png.
  • Figure 18 Core set analysis: Immune63GeneSig_CoreGenes_ICON7_SOC.png.
  • Figure 22 Kaplan Meier to show the differences in progression free survival probability between the samples predicted as Angio-Off (inactive) versus those that are predicted as Angio-On (active) by the 63 gene signature
  • Figure 23 Semi-supervised hierarchical clustering of the 529 CRC samples published by Marrisa et al (2013) using the angiogenesis defining gene list.
  • Figure 24 ROC Curve showing the discrimination in 63 gene signature scores between the angiogenesis active subtype and angiogenesis inactive subtype in the Marissa CRC data.
  • Figure 25 Kaplan Meier Curve showing the survival differences between the angiogenesis active and angiogenesis inactive patients (treated only) as predicted by the 63 gene signature in the GSE14333 CRC data.
  • biomarker panel As used herein terms "biomarker panel,” “expression classifier,” “classifier,”
  • the panel typically includes a plurality of biomarkers but may include only a single biomarker where that biomarker is useful individually in the methods of the invention.
  • a major goal of current research efforts in cancer is to increase the efficacy of perioperative systemic therapy in patients by incorporating molecular parameters into clinical therapeutic decisions.
  • Pharmacogenetics/genomics is the study of genetic/genomic factors involved in an individuals' response to a foreign compound or drug. Agents or modulators which have a stimulatory or inhibitory effect on expression of a biomarker of the invention can be administered to individuals to treat (prophylactically or therapeutically) cancer in the patient. It is ideal to also consider the pharmacogenomics of the individual in conjunction with such treatment. Differences in metabolism of therapeutics may possibly lead to severe toxicity or therapeutic failure by altering the relationship between dose and blood concentration of the pharmacologically active drug.
  • understanding the pharmacogenomics of an individual permits the selection of effective agents (e.g., drugs) for prophylactic or therapeutic treatments.
  • Such pharmacogenomics can further be used to determine appropriate dosages and therapeutic regimens.
  • the level of expression of a biomarker of the invention in an individual can be determined to thereby select appropriate agent(s) for therapeutic or prophylactic treatment of the individual.
  • the present invention relates to a molecular diagnostic tests useful for diagnosing cancers from different anatomical sites that includes the use of one or more common subtypes related to angiogenesis.
  • the invention includes expression signatures that identify a subject as having a good or poor clinical prognosis, and expression signatures that indicate whether to administer an anti-angiogenic therapeutic agent to a subject.
  • the expression signature is derived by obtaining the expression profiles of samples from a sample set of known pathology and/or clinical outcome. The samples may originate from the same sample tissue type or different tissue types.
  • an "expression profile" comprises a set of values representing the expression level for each biomarker analyzed from a given sample.
  • the expression profiles from the sample set are then analyzed using a mathematical model.
  • Different mathematical models may be applied and include, but are not limited to, models from the fields of pattern recognition (Duda et al. Pattern Classification, 2 nd ed., John Wiley, New York 2001), machine learning (Scholkopf et al. Learning with Kernels, ⁇ Press, Cambridge 2002, Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford 1995), statistics (Hastie et al. The Elements of Statistical Learning, Springer, New York 2001), bioinformatics (Dudoit et al, 2002, J. Am. Statist. Assoc. 97:77-87, Tibshirani et al, 2002, Proc. Natl.
  • the mathematical model identifies one or more biomarkers expressed in the sample set that are most predictive of a given disease phenotype. These one or more biomarkers define an expression signature. Accordingly, an expression signature includes the biomarkers identified as most predictive of a given disease phenotype.
  • the mathematical model defines a variable, such as a weight, for each identified biomarker.
  • the mathematical model defines a decision function.
  • the decision function may further define a threshold score which separates the sample set into two disease phenotypes such as, but not limited to, samples that have a good and poor clinical prognosis.
  • the decision function and expression signature are defined using a linear classifier.
  • the biomarkers defined by the expression signature also referred to as a biomarker panel
  • the new sample biomarker panel expression profile is analyzed with the same mathematical model used to define the expression signature.
  • the biomarker panel may comprise one or more of the biomarkers defined by the expression signature.
  • the biomarker panel may comprise one or more of the biomarkers defined by the expression signature.
  • the biomarker panel comprises one or more of the biomarkers listed in Table 2.
  • the biomarker panel comprises all of the biomarkers listed in Table 2.
  • the biomarker panel comprises one or more of the biomarkers listed in Table 2.
  • the biomarker panel comprises all of the biomarkers listed in Table 2.
  • the mathematical model defines an expression score for the new sample.
  • the expression score may be determined by combining the expression values of the biomarkers with corresponding scalar weights using non-linear, algebraic, trigonometric or correlative means to derive a single scalar value.
  • the expression score is compared to the threshold score and the sample classified based on whether the expression score is greater than, or equal to, or less than the threshold score.
  • a sample expression score equal to or greater than the threshold score indicates a subject has a good clinical prognosis, and a sample expression score below the threshold score indicates a subject has a poor clinical prognosis.
  • a sample expression score equal to or greater than the threshold score indicates a subject has the signature. This may indicate a good clinical prognosis.
  • a sample expression score below the threshold score indicates a subject does not have the signature. This may indicate a poor clinical prognosis.
  • One application of the expression signatures disclosed herein is the identification of patients with a good and poor clinical prognosis.
  • the good or poor prognosis may be determined in the context of a certain treatment background (such as carboplatin/paclitaxel therapy as discussed herein).
  • the subject may be receiving or have received a standard chemotherapeutic treatment for the subject's cancer type.
  • the expression signatures disclosed herein may also be used to determine whether an additional therapeutic agent, such as an anti-angiogenic therapeutic agent, should be administered to the patient.
  • an additional therapeutic agent such as an anti-angiogenic therapeutic agent
  • the present invention relates to prediction of clinical prognosis using at least progression free survival or overall survival rates. Accordingly, a "good prognosis" indicates a subject population with a cancer subtype that demonstrates an increased survival rate compared to other cancer subtypes, whereas a “poor prognosis” or “bad prognosis” indicates a subject population with a cancer subtype that demonstrates decreased survival rate compared to other cancer subtypes.
  • a subject with an expression score equal to or above the threshold score is classified as having the non-angiogenesis subtype.
  • a subject with a sample expression score above the threshold score is classified as having a good clinical prognosis.
  • a subject with a sample expression score above or equal to the threshold score indicates the subject will likely experience a detrimental effect, or have a poorer clinical prognosis, if administered an anti-angiogenic therapeutic agent.
  • the determination of a subject's clinical prognosis or selection of an additional therapeutic agent may be made in the context of past, current, or planned chemotherapeutic treament.
  • the subject may set to start, be currently receiving, or have just completed, a standard of care chemotherapeutic treatment for the cancer type of the subject.
  • the chemotherapeutic treament may include administration of an alkylating agent, an anti-metabolite, a platinum-based drug, an anti-tumor antibiotic, a topoisomerase inhibitor, a mitotic inhibitor, a corticosteroid, a hormone based therapeutic, or a combination thereof.
  • Example alkylating agents include nitrogen mustards, nitrosureas, alkyl sulfonates, triazines, and ethylenimines.
  • Example platinum drugs include cisplatin, carboplatin, and oxalaplatin.
  • Example anti-metabolites include 5-fluoruracil, 6-mercaptopurine, capecitabine, cladribine, clofarabine, cytarabine, floxuridine, fludarabine, gemcitabine, hydroxyurea, methotrexate, pemetrexed, pentostatin, and thioguanine.
  • Example anti-tumor antibiotics include daunorubicin, doxorubicin, epirubicin, idarubicin, actinomocyin- D, bleomycin, mitomycin-C, and mitoxantrone.
  • Example topoisomerase inhibtors include topotecan, irinotecan, etoposide, and teniposide.
  • Example mitotic inhibitors include taxanes, epothilones, vinca alkaloids, and estramustine.
  • Example corticosteroids include predisone, mefhylprednisolone, and dexamethasone.
  • the chemotherapy may include treatment with L-asparaginase, imatinib, gefitinib, sunitinib, bortezomib, retinoids, tretinoin, bexaroten, arsenic trioxide, fluvestrant, tamoxifen, toremifene, anastrozole, exemestane, letrozole, progestins, estrogens, bicalutamide, flutamide, nilutamide, gonadotropoin-releasing hormone agonists or analogs, rituximab, alemtuzumab, BCG, interleukin-2, interferon- alf a, thalidomide, and lenalidomide.
  • the chemotherapeutic treament may comprise a cyclosphoshamide, methotrexate, and fluorouracil (CMF) treatment regimen, a cyclophosphamide, doxorubicin, and fluorouracil (CAF) treatment regimen, an epirubicin and cyclophosphamide (EC) treatment regimen, a fluorouracil, epirubicin, and cyclophosphamide (FEC) treatment regimen, a paclitaxel and cyclophosphamide treatment regimen, a paclitaxel and carboplatin treatment regiment, a doxorubicin and cyclophosphamide treatment regiment, or a doxorubicin and paclitaxel treatment regimen.
  • the neoadjuvant cancer therapy comprises a platinum based chemotherapy treatment regimen.
  • the platinum-based chemotherapy treatement regimen comprises paclitaxel and carboplatin
  • Another application of the expression signatures disclosed herein is the stratification of response to, and selection of patients for therapeutic drug classes that encompass anti-angiogenic therapies.
  • By examining the expression of a collection of the identified biomarkers in a tumor it is possible to determine which therapeutic agent or combination of agents will be most likely to reduce the growth rate of a cancer. It is also possible to determine which therapeutic agent or combination of agents will be the least likely to reduce the growth rate of a cancer and/or which may cause adverse affects and thus be contra-indicated.
  • By examining the expression of a collection of biomarkers it is therefore possible to eliminate ineffective or inappropriate therapeutic agents.
  • these determinations can be made on a patient-by-patient basis or on an agent-by- agent basis.
  • the present invention provides a test that can guide therapy selection as well as selecting patient groups for enrichment strategies during clinical trial evaluation of novel therapeutics. For example, when evaluating a putative anti-angiogeneic agent or treatment regime, the expression signatures and methods disclosed herein may be used to select individuals for clinical trials that have cancer subtypes that are responsive to anti-angiogenic agents.
  • a cancer is "responsive" to a therapeutic agent if its rate of growth is inhibited as a result of contact with the therapeutic agent, compared to its growth in the absence of contact with the therapeutic agent.
  • Growth of a cancer can be measured in a variety of ways. For instance, the size of a tumor or measuring the expression of tumor markers appropriate for that tumor type.
  • the expression signature disclosed herein may determine a patient's clinical prognosis upon administration of an anti-angiogeneic agent following standard of care chemotherapeutic therapy for the cancer type of the patient.
  • a cancer is "non-responsive" to a therapeutic agent if its rate of growth is not inhibited, or inhibited to a very low degree, as a result of contact with the therapeutic agent when compared to its growth in the absence of contact with the therapeutic agent.
  • growth of a cancer can be measured in a variety of ways, for instance, the size of a tumor or measuring the expression of tumor markers appropriate for that tumor type.
  • the quality of being non-responsive to a therapeutic agent is a highly variable one, with different cancers exhibiting different levels of "non-responsiveness" to a given therapeutic agent, under different conditions. Still further, measures of non-responsiveness can be assessed using additional criteria beyond growth size of a tumor such as, but not limited to, patient quality of life, and degree of metastases.
  • the angiogenesis subtype can be identified from a fresh/frozen (FF) or formalin fixed paraffin embedded (FFPE) patient sample.
  • the cancer type is ovarian cancer, breast cancer, colon cancer, colorectal cancer, lung cancer, prostate cancer, or glioblastoma.
  • the cancer type is an ovarian cancer.
  • the cancer type is breast cancer.
  • the cancer type is lung cancer.
  • the cancer type is colon cancer.
  • the cancer type is prostate cancer.
  • the cancer type is glioblastoma.
  • the expression signatures of the present invention are identified by analyzing the expression profiles of certain biomarkers in a patient sample set.
  • Biomarkers suitable for use in the present invention include DNA, RNA, and proteins.
  • biomarkers suitable for use in the present invention include RNA and cDNA. The biomarkers are isolated from a patient sample and their expression levels determined to derive a set of expression profiles for each sample analyzed in the patient sample set.
  • the expression signature identifies a non-angiogenesis phenotype observed in cancer tissues, identified as a signature score for a combination of biomarkers above or equal to a threshold, the phenotype characterized by an up-regulation of immune response related genes and a down-regulation of genes associated with angiogeneisis or vasculature development related processes.
  • the expression profile obtained is a genomic or nucleic acid expression profile, where the amount or level of one or more nucleic acids in the sample is determined.
  • the sample that is assayed to generate the expression profile employed in the diagnostic or prognostic methods is a nucleic acid sample.
  • the nucleic acid sample includes a population of nucleic acids that includes the expression information of the phenotype determinative biomarkers of the cell or tissue being analyzed.
  • the nucleic acid may include RNA or DNA nucleic acids, e.g., mRNA, cRNA, cDNA etc., so long as the sample retains the expression information of the host cell or tissue from which it is obtained.
  • the sample may be prepared in a number of different ways, as is known in the art, e.g., by mRNA isolation from a cell, where the isolated mRNA is used as isolated, amplified, or employed to prepare cDNA, cRNA, etc., as is known in the field of differential gene expression. Accordingly, determining the level of mRNA in a sample includes preparing cDNA or cRNA from the mRNA and subsequently measuring the cDNA or cRNA.
  • the sample is typically prepared from a cell or tissue harvested from a subject in need of treatment, e.g., via biopsy of tissue, using standard protocols, where cell types or tissues from which such nucleic acids may be generated include any tissue in which the expression pattern of the to be determined phenotype exists, including, but not limited to, disease cells or tissue, body fluids, etc.
  • the expression profile may be generated from the initial nucleic acid sample using any convenient protocol. While a variety of different manners of generating expression profiles are known, such as those employed in the field of differential gene expression/biomarker analysis, one representative and convenient type of protocol for generating expression profiles is array-based gene expression profile generation protocols. Such applications are hybridization assays in which a nucleic acid that displays "probe" nucleic acids for each of the genes to be assayed/profiled in the profile to be generated is employed.
  • a sample of target nucleic acids is first prepared from the initial nucleic acid sample being assayed, where preparation may include labeling of the target nucleic acids with a label, e.g., a member of a signal producing system.
  • a label e.g., a member of a signal producing system.
  • the sample is contacted with the array under hybridization conditions, whereby complexes are formed between target nucleic acids that are complementary to probe sequences attached to the array surface.
  • the presence of hybridized complexes is then detected, either qualitatively or quantitatively.
  • Specific hybridization technology which may be practiced to generate the expression profiles employed in the subject methods includes the technology described in U.S. Pat. Nos.
  • hybridization conditions e.g., stringent hybridization conditions as described above, and unbound nucleic acid is then removed.
  • the resultant pattern of hybridized nucleic acids provides information regarding expression for each of the biomarkers that have been probed, where the expression information is in terms of whether or not the gene is expressed and, typically, at what level, where the expression data, i.e., expression profile, may be both qualitative and quantitative.
  • the patient sample comprises a cancer tissue samples, such as an archived sample.
  • the patient sample is preferably derived from cancer tissue and may be from a sample having been characterized by prognosis, likelihood of recurrence, long term survival, clinical outcome, treatment response, diagnosis, cancer classification, or personalized genomics profile.
  • cancer includes, but is not limited to, leukemia, brain cancer, prostate cancer, liver cancer, ovarian cancer, stomach cancer, colorectal cancer, throat cancer, breast cancer, skin cancer, melanoma, lung cancer, sarcoma, cervical cancer, testicular cancer, bladder cancer, endocrine cancer, endometrial cancer, esophageal cancer, glioma, lymphoma, neuroblastoma, osteosarcoma, pancreatic cancer, pituitary cancer, renal cancer, and the like.
  • colorectal cancer encompasses cancers that may involve cancer in tissues of both the rectum and other portions of the colon as well as cancers that may be individually classified as either colon cancer or rectal cancer.
  • the methods described herein refer to cancers that are treated with anti-angiogenic agents, anti-angiogenic targeted therapies, inhibitors of angiogenesis signaling, but not limited to these classes. These cancers also include subclasses and subtypes of these cancers at various stages of pathogenesis.
  • the patient sample comprises an ovarian cancer sample.
  • the ovarian cancer sample is a serous ovarian cancer sample such as a high grade serous ovarian cancer sample.
  • the patient sample comprises a breast cancer sample.
  • the patient sample comprises a glioblastoma sample.
  • Biological sample “sample”, and “test sample” are used interchangeably herein to refer to any material, biological fluid, tissue, or cell obtained or otherwise derived from an individual.
  • a blood sample can be fractionated into serum or into fractions containing particular types of blood cells, such as red blood cells or white blood cells (leukocytes).
  • a sample can be a combination of samples from an individual, such as a combination of a tissue and fluid sample.
  • biological sample also includes materials containing homogenized solid material, such as from a stool sample, a tissue sample, or a tissue biopsy, for example.
  • biological sample also includes materials derived from a tissue culture or a cell culture, including tissue resection and biopsy samples.
  • any suitable methods for obtaining a biological sample can be employed; example methods include, e.g., phlebotomy, swab (e.g., buccal swab), and a fine needle aspirate biopsy procedure. Samples can also be collected, e.g., by micro dissection (e.g., laser capture micro dissection (LCM) or laser micro dissection (LMD)), bladder wash, smear (e.g., a PAP smear), or ductal lavage.
  • a "biological sample” obtained or derived from an individual includes any such sample that has been processed in any suitable manner after being obtained from the individual, for example, fresh frozen or formalin fixed and/or paraffin embedded.
  • the methods of the invention as defined herein may begin with an obtained sample and thus do not necessarily incorporate the step of obtaining the sample from the patient. The methods may be in vitro methods performed on an isolated sample.
  • the term "patient” includes human and non-human animals.
  • the preferred patient for treatment is a human.
  • "Patient,” “individual” and “subject” are used interchangeably herein.
  • biomarker can refer to a gene, an mRNA, cDNA, an antisense transcript, a miRNA, a polypeptide, a protein, a protein fragment, or any other nucleic acid sequence or polypeptide sequence that indicates either gene expression levels or protein production levels.
  • a biomarker indicates or is a sign of an abnormal process, disease or other condition in an individual, that biomarker is generally described as being either over- expressed or under-expressed as compared to an expression level or value of the biomarker that indicates or is a sign of a normal process, an absence of a disease or other condition in an individual.
  • Up-regulation “up-regulated”, “over-expression”, “over-expressed”, and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that is typically detected in similar biological samples from healthy or normal individuals.
  • the terms may also refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values or levels) of the biomarker that may be detected at a different stage of a particular disease.
  • Down-regulation Down-regulated
  • under-expression under-expressed
  • any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that is typically detected in similar biological samples from healthy or normal individuals.
  • the terms may also refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values or levels) of the biomarker that may be detected at a different stage of a particular disease.
  • a biomarker that is either over-expressed or under-expressed can also be referred to as being “differentially expressed” or as having a “differential level” or “differential value” as compared to a "normal” expression level or value of the biomarker that indicates or is a sign of a normal process or an absence of a disease, disease subtype, or other condition in an individual.
  • "differential expression” of a biomarker can also be referred to as a variation from a "normal” expression level of the biomarker.
  • differential biomarker expression and “differential expression” are used interchangeably to refer to a biomarker whose expression is activated to a higher or lower level in a subject suffering from a specific disease, relative to its expression in a normal subject, or relative to its expression in a patient that responds differently to a particular therapy or has a different prognosis.
  • the terms also include biomarkers whose expression is activated to a higher or lower level at different stages of the same disease. It is also understood that a differentially expressed biomarker may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product.
  • Differential biomarker expression may include a comparison of expression between two or more genes or their gene products; or a comparison of the ratios of the expression between two or more genes or their gene products; or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disease; or between various stages of the same disease.
  • Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a biomarker among, for example, normal and diseased cells, or among cells which have undergone different disease events or disease stages.
  • the biomarker is an RNA transcript.
  • RNA transcript refers to both coding and non-coding RNA, including messenger RNAs (mRNA), alternatively spliced mRNAs, ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNAs (snRNA), and antisense RNA. Measuring mRNA in a biological sample may be used as a surrogate for detection of the level of the corresponding protein and gene in the biological sample. Thus, any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA.
  • Methods of biomarker expression profiling include, but are not limited to quantitative PCR, NGS, northern blots, southern blots, microarrays, SAGE, immunoassays (ELISA, EIA, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, flow cytometry, Luminex assay), and mass spectrometry.
  • the overall expression data for a given sample may be normalized using methods known to those skilled in the art in order to correct for differing amounts of starting material, varying efficiencies of the extraction and amplification reactions.
  • biomarkers useful for distinguishing between cancer subtypes that demonstrate a good clinical prognosis and a poor clinical prognosis can be determined by identifying biomarkers exhibiting the highest degree of variability between samples in the patient data set as determined using the expression detection methods and patient sample sets discussed above.
  • Standard statistical methods known in the art for identifying highly variable data points in expression data may be used to identify the highly variable biomarkers.
  • the significance threshold is 6.3 ⁇ 10 "5 . In another example embodiment, the significance threshold may be between 1.0 ⁇ 10 "7 to 1.0 ⁇ 10 "3 .
  • the highly variable biomarkers may be further analyzed to group samples in the patient data set into subtypes or clusters based on similar gene expression profiles.
  • biomarkers may be clustered based on how highly correlated the up-regulation or down-regulation of their expression is to one another.
  • clustering analysis techniques known in the art may be used.
  • hierarchical agglomerative clustering is used to identify the cancer subtypes.
  • the biomarkers within each cluster may be further mapped to their corresponding genes and annotated by cross-reference to one or more gene ontology databases containing information on biological activity and biological pathways associated with the gene.
  • biomarkers in clusters that are up regulated and enriched for immune response general functional terms are grouped into a putative non-angiongenesis sample group and used for expression signature generation.
  • biomarkers in clusters that are down regulated and enriched for angiogenesis and vasculature development and are up regulated and enriched for immune response general functional terms are grouped into a putative non-angiongenesis sample group and used for expression signature generation. Further details for conducting functional analysis of biomarker clusters is provided in the Examples section below.
  • the biomarkers useful in deriving an expression signature for use in the present invention are those biomarkers listed in Table 1. These biomarkers are identified as having predictive value to determine a patient response to a therapeutic agent and/or a prognostice value in identifying individuals with a good or poor clinical prognosis.
  • the expression of the biomarkers disclosed herein correlates with whether a patient will experience a detrimental or beneficial effect from administration of an anti-angiogenic therapeutic agent. By examining the expression of a collection of biomarkers, it is therefore possible to eliminate ineffective or inappropriate therapeutic agents. Importantly, in certain embodiments, these determinations can be made on a patient-by-patient basis or on an agent-by-agent basis. Thus, one can determine whether or not a particular therapeutic regimen is likely to benefit a particular patient or type of patient, and/or whether a particular regimen should be continued.
  • the expression of the biomarkers disclosed herein correlate with a patient's overall clinical prognosis.
  • a patient's overall clinical prognosis By examinaing the expression of a collection of biomarkers identified in a tumor, it is possible to determine whether the individual has a cancer subtype associated with good clinical prognosis or poor clinical prognosis.
  • these determinations can be made on a patient-by-patient basis.
  • one of ordinary skill in the art can use predicted prognosis to help select appropriate treatment regimens to treat the underlying disease while eliminating those treatment regimens most likely to produce undesired or medically unwarranted adverse side effects.
  • SEQ ID NOs listed in Table 1 refer to probe set identifiers used to measure the expression levels of the genes on an example transcriptome array.
  • Expression signatures of the present invention have been cross-validated using expression data from different arrays with differnt probe sets as detailed further in the Examples section below. Accordingly, the expression signatures and methods disclosed herein are not limited to expression values measured using the probe sets disclosed herein.
  • all or a portion of the biomarkers recited in Table 1 may be used in an expression signature.
  • expression signatures comprising the biomarkers in Table 1 can be generated using the methods provided herein and can comprise between one, and all of the markers set forth in Tables 1 and each and every combination in between (e.g., four selected markers, 16 selected markers, 74 selected markers, etc.).
  • the expression signature comprises at least 5, 10, 20, 40, 60, 100, 150, 200, or 300 or more markers.
  • the predictive biomarker panel comprises no more than 5, 10, 20, 40, 60, 100, 150, 200, 300, 400, 500, 600 or 700 markers.
  • the expression signature includes a plurality of markers listed in Table 1.
  • the expression signature includes at least about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, about 96%, about 97%, about 98%, or about 99% of the markers listed in Table 1.
  • Selected expression signatures can be assembled from the biomarkers provided using methods described herein and analogous methods known in the art.
  • the expression signature contains all genes or gene products in Table 1.
  • the following methods may be used to derive expression signatures for distinguishing between subjects that are responsive or non-responsive to anti-angiogenic therapeutics, or as prognostic indicators of certain cancer types, including expression signatures derived from the biomarkers disclosed above.
  • the expression signature is derived using a decision tree (Hastie et al. The Elements of Statistical Learning, Springer, New York 2001), a random forest (Breiman, 2001 Random Forests, Machine Learning 45:5), a neural network (Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford 1995), discriminant analysis (Duda et al.
  • Pattern Classification 2nd ed., John Wiley, New York 2001
  • PAM Prediction Analysis for Microarrays
  • SIMCA Soft Independent Modeling of Class Analogy analysis.
  • Biomarker expression values may be defined in combination with corresponding scalar weights on the real scale with varying magnitude, which are further combined through linear or non-linear, algebraic, trigonometric or correlative means into a single scalar value via an algebraic, statistical learning, Bayesian, regression, or similar algorithms which together with a mathematically derived decision function on the scalar value provide a predictive model by which expression profiles from samples may be resolved into discrete classes of responder or non-responder, resistant or non-resistant, to a specified drug, drug class, molecular subtype, or treatment regimen.
  • Such predictive models are developed by learning weights and the decision threshold, optimized for sensitivity, specificity, negative and positive predictive values, hazard ratio or any combination thereof, under cross-validation, bootstrapping or similar sampling techniques, from a set of representative expression profiles from historical patient samples with known drug response and/or resistance.
  • the biomarkers are used to form a weighted sum of their signals, where individual weights can be positive or negative.
  • the resulting sum (“expression score") is compared with a pre-determined reference point or value. The comparison with the reference point or value may be used to diagnose, or predict a clinical condition or outcome.
  • biomarkers included in the classifier provided in Tables 1 will carry unequal weights in a classifier for determining clinical prognosis. Therefore, while as few as one biomarker may be used to diagnose or predict an clinical prognosis or response to a therapeutic agent, the specificity and sensitivity or diagnosis or prediction accuracy may increase using more biomarkers.
  • weight refers to the absolute magnitude of an item in a statistical calculation.
  • the weight of each biomarker in a gene expression classifier may be determined on a data set of patient samples using learning methods known in the art.
  • bias or “offset” refers to a constant term derived using the mean expression of the signatures genes in a training set and is used to mean-center the each gene analyzed in the test dataset.
  • the expression signature is defined by a decision function.
  • a decision function is a set of weighted expression values derived using a linear classifier. All linear classifiers define the decision function using the following equation:
  • All measurement values such as the microarray gene expression intensities x for a certain sample are collected in a vector x. Each intensity is then multiplied with a corresponding weight w, to obtain the value of the decision function f(x) after adding an offset term b.
  • the linear classifier will further define a threshold value that splits the gene expression data space into two disjoint sections.
  • Example linear classifiers include but are not limited to partial least squares (PLS), (Nguyen et al, Bioinformatics 18 (2002) 39-50), support vector machines (SVM) (Scholkopf et al, Learning with Kernels, ⁇ Press, Cambridge 2002), and shrinkage discriminant analysis (SDA) (Ahdesmaki et al., Annals of applied statistics 4, 503-519 (2010)).
  • PLS partial least squares
  • SVM support vector machines
  • SDA shrinkage discriminant analysis
  • the linear classifier is a PLS linear classifier.
  • the decision function is empirically derived on a large set of training samples, for example from patients showing a good or poor clinical prognosis.
  • the threshold separates a patient group based on different characteristics such as, but not limited to, clinical prognosis before or after a given therapeutic treatment.
  • the interpretation of this quantity, i.e. the cut-off threshold is derived in the development phase ("training") from a set of patients with known outcome.
  • the corresponding weights and the responsiveness/resistance cut-off threshold for the decision score are fixed a priori from training data by methods known to those skilled in the art.
  • Partial Least Squares Discriminant Analysis (PLS-DA) is used for determining the weights. (L. Stahle, S. Wold, J. Chemom. 1 (1987) 185- 196; D. V. Nguyen, D.M. Rocke, Bioinformatics 18 (2002) 39-50).
  • the data space i.e. the set of all possible combinations of biomarker expression values
  • the data space is split into two mutually exclusive groups corresponding to different clinical classifications or predictions, for example, one corresponding to good clinical prognosis and poor clinical prognosis.
  • relative over- expression of a certain biomarker can either increase the decision score (positive weight) or reduce it (negative weight) and thus contribute to an overall decision of, for example, a good clinical prognosis.
  • the data is transformed non- linearly before applying a weighted sum as described above.
  • This non-linear transformation might include increasing the dimensionality of the data.
  • the non-linear transformation and weighted summation might also be performed implicitly, for example, through the use of a kernel function. (Scholkopf et al. Learning with Kernels, MIT Press, Cambridge 2002).
  • the patient training set data is derived by isolated RNA from a corresponding cancer tissue sample set and determining expression values by hybridizing the isolated RNA to a microarray.
  • the microarray used in deriving the expression signature is a transcriptome array.
  • a "transcriptome array" refers to a microarray containing probe sets that are designed to hybridize to sequences that have been verified as expressed in the diseased tissue of interest. Given alternative splicing and variable poly- A tail processing between tissues and biological contexts, it is possible that probes designed against the same gene sequence derived from another tissue source or biological context will not effectively bind to transcripts expressed in the diseased tissue of interest, leading to a loss of potentially relevant biological information.
  • telomere sequences are expressed in the disease tissue of interest before deriving a microarray probe set. Verification of expressed sequences in a particular disease context may be done, for example, by isolating and sequencing total RNA from a diseased tissue sample set and cross-referencing the isolated sequences with known nucleic acid sequence databases to verify that the probe set on the transcriptome array is designed against the sequences actually expressed in the diseased tissue of interest. Methods for making transcriptome arrays are described in United States Patent Application Publication No. 2006/0134663, which is incorporated herein by reference. In certain example embodiments, the probe set of the transcriptome array is designed to bind within 300 nucleotides of the 3' end of a transcript.
  • the microarray used in deriving the gene expression profiles of the present invention is the Almac Ovarian Cancer DSATM microarray (Almac Group, Craigavon, United Kingdom).
  • An optimal linear classifier can be selected by evaluating a linear classifier's performance using such diagnostics as "area under the curve” (AUC).
  • AUC refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art.
  • ROC receiver operating characteristic
  • AUC measures are useful for comparing the accuracy of a classifier across the complete data range. Linear classifiers with a higher AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., ovarian cancer samples and normal or control samples).
  • ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and/or any item of additional biomedical information) in distinguishing between two populations (e.g., individuals responding and not responding to a therapeutic agent).
  • the feature data across the entire population e.g., the cases and controls
  • the true positive and false positive rates for the data are calculated.
  • the true positive rate is determined by counting the number of cases above the value for that feature and then dividing by the total number of positive cases.
  • the false positive rate is determined by counting the number of controls above the value for that feature and then dividing by the total number of controls.
  • ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and this single sum value can be plotted in a ROC curve. Additionally, any combination of multiple features, in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test. The ROC curve is the plot of the true positive rate (sensitivity) of a test against the false positive rate (1-specificity) of the test.
  • an angiogenesis expression signature is directed to the 63 biomarkers detailed in Table 2 with corresponding ranks, and weights and associated bias detailed in the table or alternative rankings, and weightings and bias, depending, for example, on the disease setting.
  • Table 2 ranks the biomarkers in order of absolute decreasing weight, in an example classifier, in the compound decision score function.
  • the methods of the invention may rely upon measuring one or more, up to all, of the biomarkers listed in table 2.
  • the methods of the invention may comprise measuring the expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 60 or each of the biomarkers from Table 2.
  • the method may comprise measuring the expression levels of 2 to 5 of the biomarkers from Table 2.
  • an angiogenesis expression signature is directed to the 63 biomarkers detailed in Table 3 with corresponding ranks detailed in the table or alternative rankings depending, for example, on the disease setting.
  • Table 3 ranks the biomarkers in order of absolute decreasing weight, in an example classifier, in the compound decision score function.
  • the methods of the invention may rely upon measuring one or more, up to all, of the biomarkers listed in table 3.
  • the methods of the invention may comprise measuring the expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 60 or each of the biomarkers from Table 3.
  • the methods may comprise measuring the expression levels of 2 to 5 of the biomarkers from Table
  • an expression signature comprises all or a portion of the following biomarkers; IGF2, SOXll, INS, CXCL17, SLC5A1, TMEM45A, CXCR2P1, MFAP2, MATN3, RTP4, COL3A1, CDRl, RARRES3, TNFSFIO, NUAKl, SNORD114-14, SRPX, SPARC, GJB1, ⁇ 3, ISLR, TUBA1A, DEXI, BASP1, PXDN, GBP4, SLC28A3, HLA-DRA, TAP2, ACSL5, CDHll, PSMB9, MMP14, CD74, LOXLl, CIITA, ZNF697, SH3RF2, MIR198, COL1A2, TNFRSF14, COL8A1, C21orf63, TAP1, PDPN, RHOBTB3, BCL11A, HLA-DOB, XAF1, ARHGAP26, POLD2, DPYSL2, COL4A1, ID
  • an expression signature comprises IGF2, SOX11, INS, and CXCL17 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
  • an expression signature comprises IGF2, INS, SPARC, TMEM45A, COL8A1 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
  • an expression signature comprises IGF2, INS, SPARC, TMEM45A, COL8A1, COL3A1, CDRl, NUAKl, TIMP3, LOXL1 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, or 53.
  • an expression signature comprises IGF2, TIMP3, INS, CXCR2P1, NUAKl and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59.
  • an expression signature comprises IGF2, TIMP3, INS, CXCR2P1, NUAKl, CDRl, MATN3, SOX11, SNORD114-14, COL3A1 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, or 53.
  • an expression signature comprises COL3A1, SPARC, CDRl, SRPX, MATN3 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59.
  • an expression signature comprises COL3A1, SPARC, CDR1, SRPX, MATN3, TIMP3, CDH11, COL8A1, BCL11A, MMP14 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, or 53.
  • an expression signature comprises IGF2, CDR1, COL3A1, SPARC, TIMP3 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59.
  • an expression signature comprises IGF2, CDR1, COL3A1, SPARC, TIMP3, INS, COL8A1, NUAKl, MATN3, TMEM45A and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, or 59.
  • an expression signature comprises INS, SPARC, COL8A1, COL3A1, CDR1, NUAKl, TIMP3, and MMP14 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equalsl, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, or 55.
  • an expression signature comprises at least INS, SPARC, COL8A1, COL3A1, CDR1, NUAKl, TIMP3, and MMP14.
  • the expression signature comprises at least IGF2, CDR1, COL3A1, SPARC, TIMP3, INS, COL8A1, NUAKl, MATN3, TMEM45A.
  • the expression signature comprises at least IGF2, CDR1, COL3A1, SPARC, TIMP3.
  • the expression signature comprises at least, COL3A1, SPARC, CDR1, SRPX, MATN3, TIMP3, CDH11, COL8A1, BCL11A, MMP14.
  • the expression signature comprises at least COL3A1, SPARC, CDR1, SRPX, MATN3. In another example embodiment, the expression signature comprises at least COL3A1, SPARC, CDR1, SRPX, MATN3. In another example embodiment, the expression signature comprises at least IGF2, TIMP3, INS, CXCR2P1, NUAKl, CDR1, MATN3, SOX11, SNORD114-14, COL3A1. In another example embodiment, the expression signature comprises at least IGF2, TIMP3, INS, CXCR2P1, NUAKl. In another example embodiment, the expression signature comprises at least IGF2, INS, SPARC, TMEM45A, COL8A1, COL3A1, CDRl, NUAKl, TIMP3, LOXLl. In another example embodiment, the expression signature comprises at least IGF2, INS, SPARC, TMEM45A, COL8A1. In another example embodiment, the expression signature comprises at least IGF2, SOX11, INS, and CXCL17.
  • an expression signature comprises IGF2 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises SOX11 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises INS and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises CXCL17 and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises CDRl and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises COL3A1 and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises SPARC and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises TIMP3 and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises COL8A1 and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises NUAK1 and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises MATN3 and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises TMEM45A and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises CXCR2P1 and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.
  • an expression signature comprises SRPX and at least N additional biomarkers selected from the list of biomarkers in Table 2, and at least N additional biomarkers selected from the list of biomarkers in Table 2, wherein N equals 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, or 62.

Abstract

L'invention concerne des procédés pour sélectionner s'il faut administrer ou non un agent thérapeutique anti-angiogénique à un sujet, comprenant des étapes consistant à mesurer les niveaux d'expression d'un ou de plusieurs biomarqueurs choisis dans le Tableau 2 ou le Tableau 3 dans un échantillon provenant du sujet; à évaluer à partir des niveaux d'expression desdits un ou plusieurs biomarqueurs si l'échantillon provenant du sujet est positif ou négatif pour une signature de biomarqueur, un agent thérapeutique anti-angiogénique étant contre-indiqué si l'échantillon est positif pour la signature de biomarqueur. Des procédés de pronostic et des procédés de traitement associés sont également décrits. L'invention est particulièrement applicable aux cancers ovarien et colorectal.
EP15705693.8A 2014-02-07 2015-02-09 Test de diagnostic moléculaire pour prédire une réponse à des médicaments anti-angiogéniques et pronostic du cancer Withdrawn EP3102700A1 (fr)

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US20170073761A1 (en) 2017-03-16
AU2015213844A1 (en) 2016-09-15
BR112016018044A2 (pt) 2018-02-20
GB201409479D0 (en) 2014-07-09
CA2938807A1 (fr) 2015-08-13
WO2015118353A1 (fr) 2015-08-13
JP2017506506A (ja) 2017-03-09
CN106164296A (zh) 2016-11-23
KR20160117606A (ko) 2016-10-10

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